The Economics of Migrating from Web 2.0 to Web 3.0

The traffic-to-dollars business model resulted in unintended social phenomenons: social-networks and blogs, which themselves spurred new web design goals that focus on encouraging visitor participation and contribution.

The un-monetized "waste byproduct" from these websites is a stockpile of user-generated content, such as photos, videos, paintings, drawings, stories, commentary, and opinions.

To make use of all that flotsam, a suite of semantic analysis tools is being developed to organize and structure them in ways that help search engines produce better results. This is called Web 3.0--or, "the semantic web."

The consequence of Web 3.0 will be the unanticipated financial incentive for websites to monetize their content, rather than just host it to attract visitors.

The opportunity to make money from user-generated content will give incentive to visitors to produce "better" content, and for websites to be more discerning about the content they receive, which affects both the social and economic landscape of the internet.

The ways this type of transformation may evolve is the challenge for technologists and entrepreneurs, who must be both visionary in how the future may appear, but cognizant of missed opportunities of the past.

In this second installment (of three) about the consumer's role in the future of stock photography, I turn my attention to the economic effects from new developments in search technology. As I'll make clear soon, "search" is merely a spark that launches a much larger fire: the social aspects of the web. And whenever the subject of "social networks" mixes with economics, the spotlight focuses squarely on the consumer. I presented a simple example of this in part one of this series by showing that more consumers both buy and sell photos than do professional photographers or stock photo agencies, which itself has caused a dramatic shift in how licensing is done. This phenomenon is not as readily visible to the undiscerning eye, because the greatest proportion of these transactions is done on a peer-to-peer basis (directly between the photographer and the buyer). In fact, asymmetric analysis shows that approximately 80% of photo media content is acquired directly from the photographer, and images are found primarily from search engines. This mechanism creates economic incentive for those on both sides of the transaction: the buyer uses search engines to find what they want, so the seller tunes his content to conform to the kind of information that search engines look for.

The "missed opportunity" from this shift has been primarily the lack of technical infrastructure to support broad peer-to-peer licensing. Only the traditional licensing methods are available on a broad scale, which requires photos being submitted to a company (a "stock photo agency"), who then licenses them to buyers. Each individual agency operates entirely independently from others, and none of them have prominent placement in traditional search engine results, so only a small percentage of potential buyers ever end up on those sites. The majority of them go directly to the websites of the photographers themselves (because the search engines index them), but most of these photographers are unaware that they could make money licensing their content. Indeed, even social-networking sites are unaware of the financial opportunities to license the content. The net result is that few photos are actually monetized, and of those that are, the pricing is arbitrary and spurious. It's estimated the $15-20B of licensing is done on a peer-to-peer basis, and countless more dollars are simply unrealized due to this inefficiency.

I should clarify that even though my first article demonstrated this type of media growth in the photo industry, the broader market of all types of media is evolving similarly. In fact, events in online photography serves as an excellent "base case" to help forecast economic effects for other media types, such as music, video, line-art, books, and so on. What all these have in common, and which everyone has known for years, is that non-professionals create this content as well. And, people do so without necessarily intending (or expecting) make money with it. I call this class of content creators, "consumers."

By examining the photo industry, we can extrapolate what might happen with the industries of other media types. Accordingly, photography has these important characteristics:

Everyone does photography on a regular basis in large quantities.

People upload their photos the internet more frequently and in higher volumes than other media types.

Economically, photos are used more than any other media type in publishing of both commercial and editorial content. (Thus, economic value.)

More people can create "salable content" with less expertise, less effort, and greater speed than other media types.

The high quantity and low price per unit of licensed images equate to a lower barrier of entry for both buyers and sellers.

Forecasting the economic future of media on the internet is difficult because it isn't clear whether the same lack of awareness will plague other media types as it has with photography. There's already been a major economic shift in the photo industry as a result of the internet, and evidence suggests that similar economic changes are happening with video and music as well. As technology for creating content of any type improves, as does self-publishing of this content, the economics are likely to follow the trend set forth by the photo industry, and consumers will find themselves in a position to make money with their content.

The next phase of internet search technologies and standardized communication protocols may inadvertently help. As we've seen with the evolution of the internet to date, whose economic growth came from unintentional consequences, we can learn from how those circumstances came about, and use them to forecast how the future economics might also take shape.

The goal and challenge for media-oriented industries of all types is to build a more structured, formal, internet-wide framework that handles content licensing in general, regardless of the media type, or who the buyers and sellers are, and establish these foundations before consumers set precedents that are harder to unravel, which could deflate a content's value before it has a chance to flourish.

The good news is that some developments are already under way. But, to put them into context, we need to understand today's business model, and how it evolved into what we currently work with. What we'll find is a very tight sequence that starts with technical innovation, followed by social adaptation, which affects financial incentives, which comes full circle to innovation again. This feedback mechanism of constant reinforcement and revision is an economic truism. Since the technology is already underway, and some of the social fabric is similar taking shape, the only question that remains is how the business environment evolves with it.

Web 2.0 Business Models Setting the Stage

Most people are familiar with MySpace, FaceBook, and Flickr as common and well-known examples of social-networking sites. They are essentially places where people sign up and contribute "content" in the form of information about themselves, while also contributing photos, music, poetry, writing, and ideas of various sorts. In return, they get to socialize -- learn, teach, meet, and access.

Getting people to participate and contribute content is what is commonly called, "Web 2.0", and most sites on the internet are so enabled. You can visit most any blog, news organization, movie review site, shopping site, or cooking site, and you'll probably find a way to contribute something, whether it's as simple as voting on how much you liked a book, or as involved as contributing your own recipes, movies, photographs, short stories, or politically biased nonsense that you hope others will agree with.

While this kind of activity has always been technically possible to program into websites, these features of the web were largely ignored until there was a business incentive to use them. That incentive came in the form of Google's advertising network. If a site had good information, and it was indexed well by search engines, advertisers paid more dollars to have ads there. Consequently, for a site to get those advertising dollars (or to sell its own products or services), it needed to be indexed well, which means it needed more content. The easiest and cheapest way to get content is to encourage people to contribute their content. The incentive that sites give to consumers to contribute is the "social rewards."

By being smarter, funnier, cuter, or more ridiculous than others, people get attention, and people love that. So, websites used the "social" carrot to get people to participate, and in return, the site got its free content, and of course, traffic. These both raise the site's raking and boosts advertising revenue (or sales of their own stuff). Today, it's almost unheard of that sites don't have some way for users to contribute. Everyone wins.

What's notable about this development is that it was unintentional. Social websites had been around in earlier days of the net, but didn't really gather much attention or traffic. Even of those that did, it wasn't easy to make any money from those users. No one bought anything, and they wouldn't pay subscription fees. So, having millions of users did nothing but cost the company money in technical infrastructure (which itself was vastly more expensive than it is today). Companies that had stuff to sell typically didn't garner much traffic, except for dating sites, like match.com.

It wasn't until Google introduced its auction-based advertising model that inadvertently rewarded highly-trafficked sites with unanticipated revenue did a financial incentive exist. There was then an instant awareness that social-networks is where the money is.

Yet, it was never Google's intention to create social networks or any other kind. In fact, it didn't intend to affect the nature of the internet at all. It just wanted to create a model of analyzing the internet as it is (or was) for purposes of setting auction-based advertising rates. They did not anticipate that their very act of analyzing data actually changed the very data itself. (This is a perfect example of Heisenberg's Uncertainty Principle.) Indeed, not only has the data changed because Google observes it, they created a feedback mechanism where the more they looked at data (and thus, reported their observations by way of search rankings), the more the data itself morphed into to the kind that people thought Google wanted to see. This, in turn forced Google to change how it looked at the data, because people were manipulating the content on their sites to artificially bump their rankings higher.

And so it goes to this day: websites and search engines are in an endless cat and mouse game, where sites try to get higher search rankings, and for Google to maintain a plausible ranking system that users can trust to be objective when they search. This credibility is required for advertisers to trust it.

The constant feedback mechanism and subtle refinement of behaviors and economics creates a state of unpredictability.

The unpredictability invokes the Law of Unintended Consequences, which yields a new economic model.

This begs questions: What's next after Web 2.0? And what are the potential byproducts from whatever that is? More social networks? Or something else? Our objective here is to anticipate future financial opportunities without forgetting that the feedback mechanism produces unpredictable results. Also, the Law of Unintended Consequences suggests that examining user behavior changes those very behaviors, which itself often forms the basis for new developments and incentives. (Knowing the future will affect your behavior, thereby changing the future.) But, as any good entrepreneur and venture capitalist know (er, should know), the goal isn't to predict or (worse) to control or shape the future, but rather, to anticipate the parameters that are most likely to frame that future.

Web 3.0

The answer to "What's next?" is easy: Web 3.0. In inner circles, this is called, "The Semantic Web." That is, the content on the web that was generated during the Web 2.0 era will be more intelligently analyzed than before. In essence, the data is not just indexed as it is today, but is being better understood for its semantic meaning. This very core nugget of change sparks a feedback mechanism on a very large scale, which will transform the economic foundations of the internet, much the same way the web itself changed the world.

For example, let's say you have a weird and embarrassing rash. Today, searching relies on brute-force matching of search terms, such as "weird rash" or "red rash". Type that into Google, and the results you get are pages that happen to contain both words. Though the pages themselves may be ranked according to popularity (the Mayo Clinic's site may rank higher in search results than some guy's blog), you still have to sift though more than just the first set of results to find what you're really looking for.

You could do an image search for "red rash," but search engines don't really know what's in the content of photos. If you were to do such a search on images.google.com, you'll get photos of red rashes, but this isn't because Google analyzed the photos. They match because the photos happen to have the words "red" and "rash" in the image's filename. E.g., red-rash.jpg. (Google also looks at the file's pathname as well as the filename.) If you try it, you'll see there are photos of every possible kind of red rash you can get, most of them having nothing in common with the others, nor are they sorted or ranked in any intelligible way. The results are merely arbitrary listings of all matches for images with properly-named files. Google relies on the fortunate-but-useful naming convention that some people happen to use when naming their photos: that they name their files according to their content. In practicality, this technique is spurious and nearly useless, but it's the only thing they can go on for the moment. As it is today, you have to examine each photo and see whether that looks like your rash, and then examine each of the pages that the came from to determine what the rash is.

That google relies on photos' filenames to match their content is not just unreliable, it's not even complete. Statistically, most people don't change the filenames of their photos; they leave them as they were from the camera, such as DSC1004.JPG. Photos with those names are never going to come up as a search result for any search, let alone those that could be potential (and valuable) matches for relevant searches, were the search engine to genuinely know what it was searching for. So, there's a lot of photos out there that may be of red rashes, but they aren't found because there's nothing about them that indicates that's what they are.

Pro photographers may be asking, "what about metadata? What about the keywords that I apply to photos? Why doesn't Google look at that?" They don't for the same reason Google no longer looks at the "keywords" tag on html web-pages themselves when compiling results for general searches: websites learned they can game the system by stuffing these attributes with unreliable data.

People can still game the system to some degree by naming their image files accordingly, but for the moment, doing so doesn't really reward the behavior very much. It would only yield sporadic results because Google doesn't sort or prioritize results according to any sensical pattern that matters to searchers. And there's currently no other benefit to naming a photo improperly. After all, why would someone name a photo, sexy-woman.jpg if it's just a red rash?

One reason to do so would be if there were financial incentive, say, advertising revenue if your site were to get more traffic. This would provide incentive to name photos sexy-woman.jpg, even if it's a photo of a rash. As you can imagine, this would completely ruin Google's current image search feature, which is why all search engines are sort of stuck in a corner with image search results: unless they can assure some degree of reliability of results that cannot be gamed once there was a financial incentive to do so, it's best to leave the system as it is -- nearly useless, but not so much so that people don't tinker with it. Yet, there's the very paradox: because it's still the only game in town, people tinker a lot, and it's the source of most image searching on the internet today.

So, unless one can actually, reliably determine the content of a photo, image searches will have to remain circumstantial, unscientific, and without reward.

We can envision what the economic effects would be in a Web 3.0 world, where there was a better semantic understanding of media content beyond just text. Using new algorithms that can determine the content of photos, for example, you may one day search for "red rash" and get a lot more relevant search results than before, simply because the existing content on the web is better understood.

Image-recognition algorithms are evolving in many ways, and look for very different aspects of images to determine characteristics, genres, attributes and, ultimately, content. A couple examples that I often cite in my blogs are tineye.com and picscout.com, which examine photos and find "similars" on the net based on pattern recognition by identifying photos (and portions of them) by assigning a unique "fingerprint ID." Another site, xcavator.net finds photos based on conceptual elements and can do so using specific keywords, like "train" or "window." Using this technique is even better than a text search for "red rash" or "weird rash" because the image recognition algorithm can do a better job of analysis and ordering results accordingly to proximity. Here, you could just take a picture of your rash, upload it to the web, and you'll get search results that are not only more relevant, but they can see similarities and differences that humans simply can't, or would overlook by an untrained eye.

And image-recognition is only one example -- there is also music-recognition technologies that do the same sort of thing. Shazam (www.shazam.com) is a site based on music-recogition technology that is evolving its own economic model by itself. Sing a portion of a song you like into the phone (connected to the company), and it'll tell you what song it is. That's just one application of its technology, and the company is partnering with many different, diverse businesses, many having nothing to do with the web or "search engines" directly, but it is nonetheless a search technology with widespread economic effects that are, so far, unanticipated by industry watchers. A similar-but-different development from Widisoft (www.widisoft.com) does more detailed analysis of music for conversion between music file formats that assist musicians and sound engineers to better compile and arrange musical components of a song.

The social (and, by extension, economic) ramifications of all these developments should be self-evident to anyone that works in an internet or media company. Economic predictions, on the other hand, would be premature without understanding how these technologies would evolve both technically and socially.

Problems with Deployment

The first question most people ask is why aren't these websites (or their technologies) more widely deployed, or even more usefully employed, especially by larger search engines? Several reasons.

First, these algorithms are still pretty young; recognizing patterns is a difficult and imprecise science. Of course, so is traditional text search, but the difference between the two is rather substantial. (Just matching a photo with another photo -- or a song with another song -- isn't yet sufficient for a "semantic" search.)

Second, pattern-recognition of content is only the beginning. Information about what those patterns are and what they mean still need to be seeded, and that information needs to start from humans. This isn't a major barrier, as the current Web 2.0 content on the internet has a great deal of that info already. But, its vast size and disorganization means that time is required to harness it properly. During this process, major search engines are left to guess at semantic meaning from the text on the same page as a photo, for example, which is similarly error-prone and unreliable as their current method of file-naming, though a notch better. The lesson we learned about examining data altering user behavior must be heeded strongly here: if there's incentive to "lie" or manipulate data, search engines will lose credibility.

Thus, the third problem: trustworthy information about content. Because there will eventually be a financial incentive to provide trusted and controlled data feeds about various content types, new methods need to be established to "search and rank" the sources of information. That sounds similar to traditional text search and rankings of websites, but in this case, it's not the site's credibility at stake; it's the credibility of the data found on the site. Or rather, the information about the content. (That is, the description of the rash in the photo may need to be ranked, which may be independent of the site that hosts the photo.) Unlike the text on a site that can be interpreted and ranked -- which is closely linked to the site -- photos and other media types on that same website can be sourced from anywhere, and the data about that media may have well come from an entirely different source. In the Web 3.0 world, it will be much more common for crowd-sourced content to be annotated by someone other than the content's creator. (Wiki-based sites are good examples of this today.)

And lastly, the three problems noted above will be difficult to do by any one entity, since the ingredients in this recipe require participation from many different organizations. Getting that participation is difficult, especially since each is intimately focused on their own small views of the world. (This is the very problem that every player in the photo licensing industry exhibited, which is the primary cause for its slow demise.) Having an entity that sits on top of the trees and sees the broader economic opportunities that it can use to direct which direction the tribe goes in hatching through the forest is not easy, and no one is currently poised to accept such a role. It will unlikely be a small organization, and larger companies tend not to have entrepreneurial spirit or vision.

Yet, these technologies continue to develop. Just as Web 2.0 evolved relatively slowly, so too are Web 3.0 capabilities, and with them will be leading industries that pave the way for the others by establishing standards and protocols that set the economic wheel in motion. The economic wheel for Web 2.0 was advertising dollars and traffic, so people looked to Google for the parameters to design sites and user experiences that lead to traffic, so money can be made. In the 3.0 world, there is currently no such leader, since it isn't yet clear where the incentives are. Nor will such a model exist without having experienced the iterative social feedback mechanisms that are part of every economic development.

What might that social environment look like? Let's consider your rash again: You take a picture of it, upload it to an image-recognition site, which matches it to a set of potential candidates, and each one checked against a medical website that has information about the rashes, which are then fed into a pharmaceutical website, which may list potential remedies. If it turns out you just went camping over the weekend, you can assume it's likely to be poison ivy and choose the appropriate remedy. If, on the other hand, you recently visited the red light district in Bangkok, then your spouse will be alerted, and your lawyer will be notified to accept the divorce papers being prepared for you.

Such possibilities would be objectionable to many, so limitations would be naturally put into place to protect privacy. And that's just one example. As technologies develop and new capabilities are evident, people react, and social acceptance or rejection alters future developments. These must take place before effective and long-standing economic models can form.

Personalized Search

A critical component of this social evolution of Web 3.0 is found in a very old technology that hasn't been exploited to its potential: that of "predictive preferences." That is, search results being ordered according to what might be appropriate or relevant to the searcher. While many may not be aware of it, the vast amount of raw content from the Web 2.0 world is being analyzed by "crowd-analysis algorithms", which look at data that people have voted on or expressed some kind of opinion about. This data is then examined for patterns to predict how individuals might like something, which can then be used to determine whether any given search results should rise or fall in "relevance" ranking.

In the music industry, there are two applications of this technology that you may be aware of. Amazon.com has been using predictive preferences for years, and I rely on it almost exclusively when I buy music. I let amazon choose new albums for me based on what it knows about me: the things that I've bought in the past, searched for, and/or rated my preferences for as it tracks my behaviors on its site. It even looks at preferences that aren't music related. when it offers suggestions for what I'd like, I'm always shockingly surprised at its accuracy. (Porcupine Tree is my latest miraculous find.)

Another example is Pandora (www.pandora.com). People with an iPhone were recently introduced to it this way: name a song, a band, or a genre, and the site will stream music to you in radio-station format, all comprised of songs that you are likely to enjoy.

Part of how pandora does this is by applying conceptual attributes to songs, such as "acoustic guitar solo." There are over 400 such attributes, which the site calls "the music genome project." This requires humans to assign such attributes to songs manually at the moment, but music analysis isn't that hard. It isn't a stretch to envision combining these two technologies, so that an algorithm determines attributes based on digitized sound waves, which it can then assess and assign to other songs in real-time.

Hypothetically, I could rent a car in a city I've never been to and program the radio's stations by singing a song that I happen to like. The radio can be instantly programmed to assign stations to the preset buttons. No more "scan button!"

A similar "genome sequence" of photography or video has never been done (or proposed as far as I know), but it seems as one would be inevitable, and would lead to another step in the semantic understanding of media on the web. When you combine the semantic understanding of content with predictive preferences, you have a readily monetized network of resources that, currently, no one is capitalizing on.

Web 3.0 Business Model: It's the Content, Stupid

This scenario presents the potential for a pivotal economic shift of focus for where value is: from the website to content. As my earlier research in the photography realm revealed, the less time it takes for a searcher to find a relevant photo from a search, the more likely it is that the searcher will license it. There's every reason to believe that photos are not unique to this human behavior and economic need. The semantic web will make it easier to find content of any sort, and if the searcher's results are also tuned to their particular preferences, it raises the likelihood that such content would be purchased beyond the ratio we see today. Thus, the value of content on a site goes up because it has a higher chance of being monetized.

Remember all those photos named, DSC1004.JPG? That's content that is currently next to useless because it carries no semantic meaning, and is therefore not seen or understood by current search engines. The semantic web will eventually find all those abstract media objects and make sense of them, adding them to the set of possible search results. Such data exists in all media types, not just photos, making the economic possibilities far greater than anyone has anticipated. So, once all the "useless raw content" from Web 2.0 is semantically analyzed, it is likely to emerge in the Web 3.0 world as "invaluable data assets" that contribute even more to the Long Tail of internet economics.

As the perception of content's value continues to increase, Web 3.0 sites will have more incentive to attract those who create quality content.

An example illustrating this socio-economic development can be found in this story from the New York Times. Joel Moss Levinson, "a college dropout with dozens of failed jobs on his resume," has earned more than $200,000 by creating homemade movies that major corporations are now using in their mainstream commercials. Where'd they find them? YouTube. The article goes on to mention many companies getting content directly from common consumers, rather than through traditional ad agencies, and how this trend is reshaping many aspects of the Marketing and Advertising industries.

The beneficiaries of this are obviously not just limited to individuals. While Levinson created his own content, there's quite a bit of mainstream content from traditional media companies that can be applied to the same business model that benefits them: make the content available for a fee. More and more consumers are actually paying for movies and videos over the web, which is a trend that was once predicted, but failed to materialize for years. In fact, there was doubt that it would ever happen, because users just got used to the net being "free." Many early sites that tried to charge for membership found they couldn't make the numbers work. But as users are learning that good content is harder to come by, this model is finally becoming economically stable. And, it has a twist: users are not just getting content for pay, but are also given further incentives to contribute as well, such as feedback or other information about the content they're paying for. These incentives come in the form of reduced fees, or in the reduction of advertisements the visitor otherwise has to see before getting to see the content. An example is found in this article in the New York Times, where Hulu (www.hulu.com) allows visitors to view programming with fewer ads, and encourages visitors to vote on shows with thumbs-up and thumbs-down buttons.

As new and different kinds of websites are built to respond to that economic incentive, websites will continue to reinforce this behavior by adjusting compensation and other reward systems. They'll also want "semantic information" about that content, not just raw data, which changes the user experience, which changes the nature of how and why people go to websites in the first place.

Evidence of this is already making headlines: YouTube's recent announcement that they will now begin to enable users to purchase songs and other content found on the site -- whereas, before, such content was only used to attract more visitors. Similarly, Flickr's relationship with Getty Images is one where a company is cherry-picking user-generated content from a social network and selling that very same content on a professional photography site. Still another example is the growing basket of online discussion forums that are converting from "free access" to paid-for access, which is the most overt illustration where a site is changing its business model from using user-generated content to attract visitors, to one where that same content is used to generate subscription fees.

All this is part of the feedback mechanism that perpetuates unpredictable change. As users themselves are ranked and "scored" for the various content types they create and contribute, a phenomenon that already exists in many forms on social networks and discussion boards, there would be an amplification of this if there were financial incentive to raise your rankings. Or, to lower others' rankings. This type of human behavior has not yet been put to the test in a broad scale on the internet yet, so its economic effects cannot be predicted.

New Frontier for Web Design and User Participation

The economic models I described above have all been on insular sites that host content. That is, people realize that content has value, so they are using Web 2.0 world to find it. However, in the Web 3.0 world, content may very well exist on websites that don't yet have an ecommerce infrastructure. It's not just about taking credit cards or other forms of payment, it's about pricing models and legal licensing terms. This is the very inefficiency of peer-to-peer licensing that I focused on in part one of this series.

New internet-wide methods and protocols must be established to enable any website that carries licensible content. As more and better content is produced, and as search engines are better able to analyze it semantically and produce search results sorted by personalized preferences, more of the content must be licensed through a universally available infrastructure, thereby transforming the way websites are designed, further affecting the visitor behaviors and incentives.

So what about that licensing mechanism? One such development in this area is ACAP, which is found at http://www.the-acap.org/. ACAP stands for the Automated Content Access Protocol, and its main initial purpose is for communicating access and usage permissions (about the content on any given site) to web crawlers (also known as 'spiders' or 'robots'). Just as you currently accept (and need) Google and other search engines to crawl your site to index it so it will come up in search results, an ACAP search engine will do the same thing, but it looks for other details about your content besides its semantic meaning. It is used to specify license terms and conditions that the owner stipulates, should someone want to license something from your site.

Of course, this a huge and complicated effort, since mechanisms need to be put into place to track and verify content ownership. But, waving the magic wand about that for the moment, if it were to exist, this then paves the way for a content licensing protocol to sit on top of the entire stack of media and search data about it, to complete the puzzle: any content crawler could assess market conditions for any given type of media type and estimate a market value. Plug that into an existing auction-based system like Google's adwords program, and the financial models are in place for the new economic model where a series of automated analytical robots crawl the web, analyze content, rate and rank its information and its creators, and come up with a high/low range for pricing, which can be used to see a more fine-tuned auction-based mechanism.

As futuristic as this may sounds, all of the technologies that do these tasks exist today in one form or another. It's merely applying them in a generalized way to arbitrary and abstract data types that makes it an inevitable development. What's more, it's self-regulating and self-perpetuating. Taken out of the equation is the inefficiencies of peer-to-peer licensing models, where prices are arbitrary, and the transaction itself is costly and time-consuming.

Just as Web 2.0 created a feedback mechanism (where social networks yielded financial returns, which stimulated the growth of social networks), the Web 3.0 world will have a similar feedback mechanism, where content creators are given incentives to create good content, describe it well, and allow third-party, automated market-makers to handle transactions. Though the content itself may still be exchanged between creators and publishers, the transaction will more likely be officiated through market-makers.

It's also a more efficient system in that incentives to cheat are reduced. This comes in two forms. First, because everyone's search may not necessarily yield the same results, attempting to manipulate content to match what someone might think search engines are looking for may actually diminish the content's value. Searchers looking for a photo of a "woman" aren't always looking for porn -- they may genuinely be looking for a photo to be used in legitimate mainstream media. If the content creator tries to "lie" to manipulate search engine results for the photo, he may inadvertently eliminate as many buyers as he would attract if he were just honest about the content in the first place. That's not to say that all content is equally valued, but that brings up the second aspect to semantic awareness by search engines: the content itself would be ranked, not just the site it came from. If a particular set of photos were manipulated with "keyword pollution" (where the photographer adds a huge amount of keywords in the hopes of being indexed to match a large number of search parameters), then that image would be reduced in its credibility ranking, irrespective of what the photo's content actually depicted, or what site the photo came from. Being a bad actor in the economic game has penalties, and being a good actor has rewards.

The Spoiler

So, what can disrupt this potential future? The elephant in the middle of the room that I haven't mentioned is Copyright. That is, user-generated content is copyrighted material, owned by the creators of content, and that creator has rights. The ability for a website to sell content that visitors submit is restricted in ways that aren't entirely easy for everyone to quickly understand, and navigating around this restriction involves an exercise in skills in three disciplines: political, legal and socio-economic. I'll tackle all that and more in part three of this series. Stay Tuned.